Cambridge University Press

Natural Language Engineering Template

Write in a clean editor, then format for Natural Language Engineering in one click — DocuGuru applies the official Cambridge University Press template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.

About the Natural Language Engineering format

Natural Language Engineering is a peer-reviewed journal published by Cambridge University Press, covering Natural Language Processing Techniques, Topic Modeling, Speech and dialogue systems.

PublisherCambridge University Press
Reference styleNumbered
Numbered — [1], [2] in the text
[1] A. Smith, B. Jones, and C. Lee, A representative article title, Natural Language Engineering 12 (2023) 45–58.

Formats any DOI in the closest standard style — Natural Language Engineering has no published style definition, so this is an approximation. No sign-up.

Publishes research inNatural Language Processing Techniques Topic Modeling Speech and dialogue systems Diverse Scientific and Economic Studies Educational Robotics and Engineering
ISSN1351-3249
Citation impact (2-yr)7.5
h-index85
i10-index421
Total citations31,146
Top institutions publishing hereUniversity of Sheffield
Journal websitewww.cambridge.org
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in Natural Language Engineering per year

41
2013
22
2014
42
2015
39
2016
44
2017
51
2018
66
2019
62
2020
39
2021
66
2022
52
2023
12
2024

Citation impact of Natural Language Engineering by publication year

486
2013
324
2014
819
2015
1.9K
2016
770
2017
577
2018
1K
2019
1.1K
2020
313
2021
612
2022
364
2023
88
2024

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in Natural Language Engineering

Word2Vec

Kenneth Church · 16 Dec 2016

Abstract My last column ended with some comments about Kuhn and word2vec. Word2vec has racked up plenty of citations because it satisifies both of Kuhn’s conditions for emerging trends: (1) a few initial (promising, if not convincing) successes that motivate early adopters (students) to do more, as well as (2) leaving plenty of room for…

UIMA: an architectural approach to unstructured information processing in the corporate research environment

David Ferrucci, Adam Lally · 1 Sep 2004

IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly…

Technical terminology: some linguistic properties and an algorithm for identification in text

John S. Justeson, Slava M. Katz · 1 Mar 1995

Abstract This paper identifies some linguistic properties of technical terminology, and uses them to formulate an algorithm for identifying technical terms in running text. The grammatical properties discussed are preferred phrase structures: technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally prepositions; rerely do terms contain verbs, adverbs, or conjunctions. The discourse…

MaltParser: A language-independent system for data-driven dependency parsing

Joakim Nivre, Johan Hall, Jens Nilsson et al. · 12 Jan 2007

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that…

Natural language interfaces to databases – an introduction

Ion Androutsopoulos, Graéme Ritchie, Peter Thanisch · 1 Mar 1995

Abstract This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBS have to…

Natural Language Engineering template — frequently asked questions

How do I write a paper in the Natural Language Engineering format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the Natural Language Engineering template. When you export, DocuGuru compiles the paper into the official Cambridge University Press format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does Natural Language Engineering use?
Natural Language Engineering uses Numbered references, shown as numbered [1], [2] markers in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: [1] A. Smith, B. Jones, and C. Lee, A representative article title, Natural Language Engineering 12 (2023) 45–58.
Do I need to know LaTeX to submit to Natural Language Engineering?
No. DocuGuru generates the null LaTeX class and compiles the PDF for you in the background, so you get a Cambridge University Press-ready Natural Language Engineering document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the Natural Language Engineering template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the Natural Language Engineering format with correct headings, figures, tables, and numbered citations.
Who publishes Natural Language Engineering?
Natural Language Engineering is a multidisciplinary journal published by Cambridge University Press. DocuGuru's Natural Language Engineering template matches Cambridge University Press's official submission format.
Can I export a submission-ready Natural Language Engineering PDF?
Yes — DocuGuru produces a PDF built with the official Natural Language Engineering template (the null class) that is ready to submit to Cambridge University Press, together with the matching LaTeX source files.
How much does the Natural Language Engineering template cost?
You can start writing in the Natural Language Engineering template for free. Exporting the final submission-ready Natural Language Engineering PDF and LaTeX source is part of DocuGuru's paid plans — see the app for current pricing.
Use the Natural Language Engineering template